High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

APDe-MVS		91.22 24	91.92 13	89.14 67	92.97 83	78.04 93	92.84 16	94.14 35	83.33 51	93.90 25	95.73 27	88.77 26	96.41 1	87.60 16	97.98 44	92.98 142

MSP-MVS		89.08 65	88.16 78	91.83 21	95.76 18	86.14 24	92.75 17	93.90 46	78.43 112	89.16 122	92.25 145	72.03 217	96.36 2	88.21 8	90.93 256	92.98 142
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DPE-MVS		90.53 35	91.08 36	88.88 69	93.38 73	78.65 89	89.15 80	94.05 40	84.68 38	93.90 25	94.11 86	88.13 36	96.30 3	84.51 61	97.81 55	91.70 195
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SteuartSystems-ACMMP		91.16 26	91.36 26	90.55 42	93.91 61	80.97 68	91.49 38	93.48 63	82.82 58	92.60 55	93.97 91	88.19 33	96.29 4	87.61 15	98.20 32	94.39 86
Skip Steuart: Steuart Systems R&D Blog.
testtj		89.51 57	89.48 60	89.59 59	92.26 103	80.80 69	90.14 56	93.54 61	83.37 50	90.57 90	92.55 134	84.99 68	96.15 5	81.26 93	96.61 102	91.83 191

ZD-MVS					92.22 106	80.48 70		91.85 118	71.22 205	90.38 91	92.98 117	86.06 63	96.11 6	81.99 87	96.75 98

SMA-MVS		90.31 38	90.48 49	89.83 53	95.31 31	79.52 80	90.98 43	93.24 77	75.37 149	92.84 49	95.28 37	85.58 66	96.09 7	87.92 10	97.76 57	93.88 106
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
MSC_two_6792asdad				88.81 71	91.55 133	77.99 94		91.01 142					96.05 8	87.45 19	98.17 33	92.40 167

No_MVS				88.81 71	91.55 133	77.99 94		91.01 142					96.05 8	87.45 19	98.17 33	92.40 167

DTE-MVSNet		89.98 47	91.91 15	84.21 159	96.51 8	57.84 292	88.93 84	92.84 95	91.92 3	96.16 3	96.23 18	86.95 50	95.99 10	79.05 120	98.57 15	98.80 6

PGM-MVS		91.20 25	90.95 42	91.93 15	95.67 23	85.85 30	90.00 57	93.90 46	80.32 86	91.74 71	94.41 68	88.17 34	95.98 11	86.37 37	97.99 42	93.96 103

APD-MVS		89.54 56	89.63 57	89.26 65	92.57 92	81.34 66	90.19 55	93.08 83	80.87 81	91.13 79	93.19 112	86.22 61	95.97 12	82.23 85	97.18 85	90.45 225
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		88.14 76	87.82 81	89.09 68	95.72 22	76.74 116	92.49 25	91.19 138	67.85 242	86.63 168	94.84 49	79.58 136	95.96 13	87.62 14	94.50 181	94.56 77
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 34	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 14	85.07 53	99.27 1	99.54 1

WR-MVS_H		89.91 50	91.31 31	85.71 131	96.32 10	62.39 243	89.54 72	93.31 70	90.21 10	95.57 9	95.66 30	81.42 117	95.90 15	80.94 98	98.80 3	98.84 5

DVP-MVS++		90.07 42	91.09 35	87.00 101	91.55 133	72.64 144	96.19 2	94.10 38	85.33 32	93.49 38	94.64 58	81.12 120	95.88 16	87.41 21	95.94 132	92.48 163

test_0728_SECOND				86.79 105	94.25 49	72.45 152	90.54 47	94.10 38					95.88 16	86.42 35	97.97 45	92.02 184

ZNCC-MVS		91.26 23	91.34 29	91.01 35	95.73 21	83.05 54	92.18 28	94.22 27	80.14 89	91.29 78	93.97 91	87.93 40	95.87 18	88.65 4	97.96 47	94.12 98

region2R		91.44 20	91.30 32	91.87 19	95.75 19	85.90 28	92.63 21	93.30 72	81.91 68	90.88 86	94.21 78	87.75 41	95.87 18	87.60 16	97.71 61	93.83 108

ACMMPR		91.49 17	91.35 28	91.92 16	95.74 20	85.88 29	92.58 22	93.25 76	81.99 66	91.40 75	94.17 82	87.51 44	95.87 18	87.74 11	97.76 57	93.99 101

3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 142	81.66 64	91.25 40	94.13 36	88.89 13	88.83 127	94.26 76	77.55 153	95.86 21	84.88 56	95.87 136	95.24 58

SED-MVS		90.46 37	91.64 19	86.93 102	94.18 50	72.65 142	90.47 50	93.69 54	83.77 44	94.11 23	94.27 73	90.28 15	95.84 22	86.03 45	97.92 48	92.29 174

test_241102_TWO								93.71 53	83.77 44	93.49 38	94.27 73	89.27 22	95.84 22	86.03 45	97.82 54	92.04 183

GST-MVS		90.96 28	91.01 39	90.82 38	95.45 28	82.73 57	91.75 36	93.74 52	80.98 79	91.38 76	93.80 101	87.20 48	95.80 24	87.10 31	97.69 62	93.93 104

XVS		91.54 15	91.36 26	92.08 10	95.64 24	86.25 21	92.64 19	93.33 67	85.07 35	89.99 99	94.03 88	86.57 56	95.80 24	87.35 23	97.62 65	94.20 92

X-MVStestdata		85.04 124	82.70 168	92.08 10	95.64 24	86.25 21	92.64 19	93.33 67	85.07 35	89.99 99	16.05 374	86.57 56	95.80 24	87.35 23	97.62 65	94.20 92

DVP-MVS		90.06 43	91.32 30	86.29 115	94.16 53	72.56 148	90.54 47	91.01 142	83.61 47	93.75 31	94.65 55	89.76 19	95.78 27	86.42 35	97.97 45	90.55 223
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									85.33 32	93.75 31	94.65 55	87.44 45	95.78 27	87.41 21	98.21 30	92.98 142

DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 63	93.64 68	79.07 84	88.54 92	94.20 28	73.53 168	89.71 108	94.82 50	85.09 67	95.77 29	84.17 64	98.03 39	93.26 132
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS		92.13 9	92.20 11	91.91 17	95.58 26	84.67 43	93.51 8	94.85 16	82.88 57	91.77 70	93.94 98	90.55 13	95.73 30	88.50 7	98.23 29	95.33 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_part1		87.15 89	87.82 81	85.15 140	88.88 192	63.04 233	87.98 97	94.85 16	82.52 61	93.61 37	95.73 27	67.51 237	95.71 31	80.48 106	98.83 2	96.69 26

CP-MVS		91.67 14	91.58 21	91.96 14	95.29 32	87.62 12	93.38 9	93.36 65	83.16 53	91.06 81	94.00 90	88.26 32	95.71 31	87.28 26	98.39 21	92.55 160

SR-MVS		92.23 8	92.34 9	91.91 17	94.89 39	87.85 11	92.51 24	93.87 49	88.20 21	93.24 42	94.02 89	90.15 17	95.67 33	86.82 32	97.34 80	92.19 180

bld_raw_conf005		88.83 70	88.48 75	89.85 52	92.53 94	76.54 117	91.30 39	93.28 75	74.96 153	93.26 41	96.02 23	70.41 225	95.63 34	86.73 33	97.87 52	97.39 14

ACMMP		91.91 12	91.87 17	92.03 13	95.53 27	85.91 27	93.35 11	94.16 31	82.52 61	92.39 59	94.14 84	89.15 23	95.62 35	87.35 23	98.24 28	94.56 77
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
PEN-MVS		90.03 45	91.88 16	84.48 151	96.57 6	58.88 284	88.95 82	93.19 78	91.62 4	96.01 6	96.16 20	87.02 49	95.60 36	78.69 123	98.72 9	98.97 3

PS-CasMVS		90.06 43	91.92 13	84.47 152	96.56 7	58.83 287	89.04 81	92.74 98	91.40 5	96.12 4	96.06 22	87.23 47	95.57 37	79.42 118	98.74 6	99.00 2

HFP-MVS		91.30 21	91.39 25	91.02 33	95.43 29	84.66 44	92.58 22	93.29 73	81.99 66	91.47 73	93.96 94	88.35 30	95.56 38	87.74 11	97.74 59	92.85 146

#test#		90.49 36	90.31 51	91.02 33	95.43 29	84.66 44	90.65 45	93.29 73	77.00 126	91.47 73	93.96 94	88.35 30	95.56 38	84.88 56	97.74 59	92.85 146

RPMNet		78.88 222	78.28 229	80.68 229	79.58 322	62.64 239	82.58 205	94.16 31	74.80 154	75.72 309	92.59 131	48.69 323	95.56 38	73.48 183	82.91 333	83.85 305

RRT_MVS		83.25 165	81.08 193	89.74 54	80.55 316	79.32 81	86.41 125	86.69 228	72.33 191	87.00 159	91.08 173	44.98 347	95.55 41	84.47 62	96.24 120	94.36 88

CP-MVSNet		89.27 61	90.91 43	84.37 153	96.34 9	58.61 289	88.66 91	92.06 112	90.78 6	95.67 7	95.17 41	81.80 113	95.54 42	79.00 121	98.69 10	98.95 4

LPG-MVS_test		91.47 19	91.68 18	90.82 38	94.75 42	81.69 61	90.00 57	94.27 23	82.35 63	93.67 34	94.82 50	91.18 5	95.52 43	85.36 51	98.73 7	95.23 59

LGP-MVS_train				90.82 38	94.75 42	81.69 61		94.27 23	82.35 63	93.67 34	94.82 50	91.18 5	95.52 43	85.36 51	98.73 7	95.23 59

test1172		92.40 7	92.41 7	92.37 6	94.68 45	89.04 6	91.98 31	93.62 57	90.14 11	93.63 36	94.16 83	88.83 24	95.51 45	87.11 30	97.54 73	92.54 161

SR-MVS-dyc-post		92.41 6	92.41 7	92.39 5	94.13 55	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 65	88.83 24	95.51 45	87.16 28	97.60 67	92.73 151

mPP-MVS		91.69 13	91.47 24	92.37 6	96.04 13	88.48 10	92.72 18	92.60 101	83.09 54	91.54 72	94.25 77	87.67 43	95.51 45	87.21 27	98.11 36	93.12 137

test_241102_ONE					94.18 50	72.65 142		93.69 54	83.62 46	94.11 23	93.78 104	90.28 15	95.50 48

DROMVSNet		88.01 78	88.32 77	87.09 100	89.28 183	72.03 158	90.31 53	96.31 3	80.88 80	85.12 195	89.67 211	84.47 74	95.46 49	82.56 80	96.26 119	93.77 114

ACMMP_NAP		90.65 31	91.07 38	89.42 62	95.93 16	79.54 79	89.95 60	93.68 56	77.65 119	91.97 67	94.89 47	88.38 28	95.45 50	89.27 3	97.87 52	93.27 131

CANet		83.79 155	82.85 166	86.63 107	86.17 249	72.21 157	83.76 171	91.43 129	77.24 124	74.39 319	87.45 246	75.36 174	95.42 51	77.03 147	92.83 217	92.25 178

MP-MVS		91.14 27	90.91 43	91.83 21	96.18 11	86.88 16	92.20 27	93.03 87	82.59 60	88.52 133	94.37 72	86.74 53	95.41 52	86.32 38	98.21 30	93.19 135
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LS3D		90.60 33	90.34 50	91.38 27	89.03 188	84.23 47	93.58 6	94.68 19	90.65 7	90.33 93	93.95 97	84.50 72	95.37 53	80.87 99	95.50 149	94.53 80

HPM-MVS_fast		92.50 5	92.54 6	92.37 6	95.93 16	85.81 32	92.99 13	94.23 26	85.21 34	92.51 56	95.13 42	90.65 10	95.34 54	88.06 9	98.15 35	95.95 41

NCCC		87.36 86	86.87 97	88.83 70	92.32 102	78.84 87	86.58 122	91.09 140	78.77 108	84.85 201	90.89 182	80.85 123	95.29 55	81.14 95	95.32 154	92.34 170

EPP-MVSNet		85.47 115	85.04 128	86.77 106	91.52 136	69.37 180	91.63 37	87.98 208	81.51 73	87.05 158	91.83 154	66.18 245	95.29 55	70.75 207	96.89 91	95.64 46

zzz-MVS		91.27 22	91.26 33	91.29 29	96.59 4	86.29 19	88.94 83	91.81 121	84.07 40	92.00 65	94.40 69	86.63 54	95.28 57	88.59 5	98.31 24	92.30 172

MTAPA		91.52 16	91.60 20	91.29 29	96.59 4	86.29 19	92.02 30	91.81 121	84.07 40	92.00 65	94.40 69	86.63 54	95.28 57	88.59 5	98.31 24	92.30 172

HQP_MVS		87.75 85	87.43 88	88.70 77	93.45 70	76.42 121	89.45 75	93.61 58	79.44 97	86.55 169	92.95 120	74.84 180	95.22 59	80.78 101	95.83 137	94.46 82

plane_prior5								93.61 58					95.22 59	80.78 101	95.83 137	94.46 82

ACMP	79.16 10	90.54 34	90.60 48	90.35 46	94.36 47	80.98 67	89.16 79	94.05 40	79.03 104	92.87 47	93.74 105	90.60 12	95.21 61	82.87 77	98.76 4	94.87 67
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DeepC-MVS_fast	80.27 8	86.23 103	85.65 119	87.96 92	91.30 140	76.92 113	87.19 108	91.99 114	70.56 211	84.96 197	90.69 188	80.01 133	95.14 62	78.37 125	95.78 142	91.82 192
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETV-MVS		84.31 139	83.91 154	85.52 134	88.58 197	70.40 173	84.50 153	93.37 64	78.76 109	84.07 219	78.72 344	80.39 129	95.13 63	73.82 179	92.98 214	91.04 207

abl_6		93.02 4	93.16 4	92.60 4	94.73 44	88.99 7	93.26 12	94.19 30	89.11 12	94.43 16	95.27 38	91.86 3	95.09 64	87.54 18	98.02 40	93.71 116

APD-MVS_3200maxsize		92.05 10	92.24 10	91.48 24	93.02 81	85.17 36	92.47 26	95.05 15	87.65 24	93.21 43	94.39 71	90.09 18	95.08 65	86.67 34	97.60 67	94.18 94

HPM-MVS++		88.93 68	88.45 76	90.38 45	94.92 37	85.85 30	89.70 64	91.27 135	78.20 114	86.69 167	92.28 144	80.36 130	95.06 66	86.17 43	96.49 107	90.22 228

MP-MVS-pluss		90.81 29	91.08 36	89.99 51	95.97 14	79.88 74	88.13 96	94.51 21	75.79 142	92.94 45	94.96 45	88.36 29	95.01 67	90.70 2	98.40 20	95.09 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CDPH-MVS		86.17 106	85.54 120	88.05 91	92.25 104	75.45 126	83.85 167	92.01 113	65.91 255	86.19 176	91.75 158	83.77 81	94.98 68	77.43 143	96.71 99	93.73 115

COLMAP_ROB	83.01 3	91.97 11	91.95 12	92.04 12	93.68 67	86.15 23	93.37 10	95.10 14	90.28 9	92.11 62	95.03 44	89.75 21	94.93 69	79.95 109	98.27 27	95.04 64
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
IS-MVSNet		86.66 96	86.82 99	86.17 122	92.05 112	66.87 203	91.21 41	88.64 194	86.30 30	89.60 115	92.59 131	69.22 229	94.91 70	73.89 177	97.89 51	96.72 25

ETH3 D test6400		85.09 122	84.87 131	85.75 130	90.80 156	69.34 181	85.90 130	93.31 70	65.43 262	86.11 179	89.95 205	80.92 122	94.86 71	75.90 159	95.57 147	93.05 139

OurMVSNet-221017-0		90.01 46	89.74 55	90.83 37	93.16 79	80.37 71	91.91 34	93.11 80	81.10 77	95.32 10	97.24 5	72.94 205	94.85 72	85.07 53	97.78 56	97.26 16

test12				86.57 108	90.74 157	72.63 146		90.69 150		82.76 235		79.20 137	94.80 73		95.32 154	92.27 176

SixPastTwentyTwo		87.20 88	87.45 87	86.45 111	92.52 95	69.19 186	87.84 101	88.05 205	81.66 71	94.64 14	96.53 14	65.94 246	94.75 74	83.02 76	96.83 95	95.41 51

CNVR-MVS		87.81 84	87.68 83	88.21 88	92.87 85	77.30 109	85.25 139	91.23 136	77.31 123	87.07 157	91.47 164	82.94 89	94.71 75	84.67 59	96.27 118	92.62 158

OPU-MVS				88.27 87	91.89 118	77.83 97	90.47 50				91.22 168	81.12 120	94.68 76	74.48 170	95.35 152	92.29 174

K. test v3		85.14 120	84.73 133	86.37 112	91.13 148	69.63 179	85.45 137	76.68 304	84.06 42	92.44 58	96.99 8	62.03 265	94.65 77	80.58 104	93.24 206	94.83 73

xxxxxxxxxxxxxcwj		89.04 66	89.13 64	88.79 73	93.75 64	77.44 104	86.31 126	95.27 12	70.80 208	92.28 60	93.80 101	86.89 51	94.64 78	85.52 49	97.51 75	94.30 90

SF-MVS		90.27 39	90.80 45	88.68 78	92.86 87	77.09 111	91.19 42	95.74 5	81.38 74	92.28 60	93.80 101	86.89 51	94.64 78	85.52 49	97.51 75	94.30 90

HQP4-MVS										80.56 268			94.61 80			93.56 125

HQP-MVS		84.61 131	84.06 150	86.27 116	91.19 144	70.66 171	84.77 143	92.68 99	73.30 174	80.55 269	90.17 203	72.10 213	94.61 80	77.30 144	94.47 182	93.56 125

PS-MVSNAJss		88.31 74	87.90 80	89.56 60	93.31 75	77.96 96	87.94 99	91.97 115	70.73 210	94.19 22	96.67 11	76.94 162	94.57 82	83.07 74	96.28 116	96.15 33

DeepPCF-MVS	81.24 5	87.28 87	86.21 107	90.49 43	91.48 137	84.90 39	83.41 182	92.38 106	70.25 216	89.35 120	90.68 189	82.85 90	94.57 82	79.55 114	95.95 131	92.00 185

UA-Net		91.49 17	91.53 22	91.39 26	94.98 36	82.95 56	93.52 7	92.79 96	88.22 20	88.53 132	97.64 2	83.45 84	94.55 84	86.02 47	98.60 13	96.67 27

CS-MVS		88.14 76	87.67 84	89.54 61	89.56 178	79.18 83	90.47 50	94.77 18	79.37 99	84.32 210	89.33 216	83.87 77	94.53 85	82.45 81	94.89 171	94.90 65

CS-MVS-test		87.00 90	86.43 102	88.71 76	89.46 179	77.46 103	89.42 77	95.73 6	77.87 117	81.64 255	87.25 250	82.43 95	94.53 85	77.65 138	96.46 109	94.14 97

iter_conf05		78.81 224	77.35 237	83.21 181	82.98 292	60.75 265	84.09 157	88.34 199	63.12 275	84.25 218	89.48 213	31.41 371	94.51 87	76.64 150	95.83 137	94.38 87

114514_t		83.10 169	82.54 173	84.77 146	92.90 84	69.10 188	86.65 120	90.62 153	54.66 326	81.46 257	90.81 185	76.98 161	94.38 88	72.62 194	96.18 121	90.82 214

MVSFormer		82.23 178	81.57 187	84.19 161	85.54 255	69.26 183	91.98 31	90.08 172	71.54 200	76.23 303	85.07 287	58.69 286	94.27 89	86.26 39	88.77 281	89.03 249

test_djsdf		89.62 54	89.01 66	91.45 25	92.36 99	82.98 55	91.98 31	90.08 172	71.54 200	94.28 21	96.54 13	81.57 115	94.27 89	86.26 39	96.49 107	97.09 20

原ACMM1				84.60 150	92.81 90	74.01 133		91.50 127	62.59 278	82.73 236	90.67 190	76.53 169	94.25 91	69.24 220	95.69 145	85.55 286

ETH3D cwj APD-0.16		87.83 83	87.62 85	88.47 81	91.21 143	78.20 91	87.26 107	94.54 20	72.05 196	88.89 124	92.31 142	83.86 78	94.24 92	81.59 92	96.87 92	92.97 145

AdaColmap		83.66 157	83.69 156	83.57 175	90.05 173	72.26 155	86.29 128	90.00 174	78.19 115	81.65 254	87.16 252	83.40 85	94.24 92	61.69 278	94.76 177	84.21 300

Effi-MVS+-dtu		85.82 111	83.38 158	93.14 3	87.13 226	91.15 2	87.70 102	88.42 196	74.57 157	83.56 226	85.65 272	78.49 143	94.21 94	72.04 198	92.88 216	94.05 100

EIA-MVS		82.19 179	81.23 191	85.10 141	87.95 209	69.17 187	83.22 190	93.33 67	70.42 212	78.58 287	79.77 341	77.29 155	94.20 95	71.51 201	88.96 279	91.93 189

UniMVSNet (Re)		86.87 91	86.98 95	86.55 109	93.11 80	68.48 190	83.80 170	92.87 92	80.37 84	89.61 114	91.81 156	77.72 150	94.18 96	75.00 169	98.53 16	96.99 23

PHI-MVS		86.38 100	85.81 114	88.08 89	88.44 201	77.34 107	89.35 78	93.05 84	73.15 179	84.76 202	87.70 241	78.87 140	94.18 96	80.67 103	96.29 115	92.73 151

test_prior3		86.31 101	86.31 104	86.32 113	90.59 161	71.99 159	83.37 183	92.85 93	75.43 146	84.58 204	91.57 160	81.92 110	94.17 98	79.54 115	96.97 89	92.80 148

test_prior				86.32 113	90.59 161	71.99 159		92.85 93					94.17 98			92.80 148

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 15	90.26 3	95.70 4	96.46 2	90.58 8	92.86 48	96.29 16	88.16 35	94.17 98	86.07 44	98.48 18	97.22 18

tttt0517		81.07 193	79.58 214	85.52 134	88.99 190	66.45 206	87.03 112	75.51 312	73.76 166	88.32 139	90.20 200	37.96 363	94.16 101	79.36 119	95.13 161	95.93 42

v7n		90.13 40	90.96 41	87.65 95	91.95 114	71.06 169	89.99 59	93.05 84	86.53 28	94.29 19	96.27 17	82.69 91	94.08 102	86.25 41	97.63 64	97.82 8

v10		86.54 97	87.10 91	84.84 144	88.16 207	63.28 230	86.64 121	92.20 109	75.42 148	92.81 51	94.50 61	74.05 190	94.06 103	83.88 66	96.28 116	97.17 19

UniMVSNet_NR-MVSNet		86.84 93	87.06 92	86.17 122	92.86 87	67.02 201	82.55 207	91.56 125	83.08 55	90.92 83	91.82 155	78.25 146	93.99 104	74.16 173	98.35 22	97.49 13

DU-MVS		86.80 94	86.99 94	86.21 120	93.24 77	67.02 201	83.16 191	92.21 108	81.73 70	90.92 83	91.97 149	77.20 156	93.99 104	74.16 173	98.35 22	97.61 10

mvs-test1		84.55 133	82.12 177	91.84 20	87.13 226	89.54 4	85.05 142	88.42 196	74.57 157	80.60 266	82.98 308	78.49 143	93.98 106	72.04 198	89.77 270	92.00 185

DP-MVS Recon		84.05 149	83.22 160	86.52 110	91.73 125	75.27 127	83.23 189	92.40 104	72.04 197	82.04 245	88.33 231	77.91 149	93.95 107	66.17 245	95.12 163	90.34 227

h-mvs33		84.25 142	82.76 167	88.72 75	91.82 124	82.60 58	84.00 162	84.98 255	71.27 202	86.70 165	90.55 193	63.04 261	93.92 108	78.26 129	94.20 188	89.63 235

DP-MVS		88.60 72	89.01 66	87.36 98	91.30 140	77.50 102	87.55 103	92.97 90	87.95 22	89.62 112	92.87 123	84.56 71	93.89 109	77.65 138	96.62 101	90.70 217

NR-MVSNet		86.00 107	86.22 106	85.34 137	93.24 77	64.56 218	82.21 219	90.46 155	80.99 78	88.42 135	91.97 149	77.56 152	93.85 110	72.46 196	98.65 12	97.61 10

EPNet		80.37 207	78.41 228	86.23 117	76.75 342	73.28 137	87.18 109	77.45 299	76.24 132	68.14 342	88.93 224	65.41 248	93.85 110	69.47 218	96.12 125	91.55 200
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		89.80 51	89.97 52	89.27 64	94.76 41	79.86 75	86.76 118	92.78 97	78.78 107	92.51 56	93.64 107	88.13 36	93.84 112	84.83 58	97.55 70	94.10 99
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ETH3D-3000-0.1		88.85 69	88.96 69	88.52 79	91.94 116	77.27 110	88.71 89	95.26 13	76.08 133	90.66 89	92.69 129	84.48 73	93.83 113	83.38 71	97.48 77	94.47 81

9.14			89.29 62		91.84 122		88.80 87	95.32 11	75.14 151	91.07 80	92.89 122	87.27 46	93.78 114	83.69 69	97.55 70

TranMVSNet+NR-MVSNet		87.86 81	88.76 73	85.18 139	94.02 58	64.13 222	84.38 154	91.29 134	84.88 37	92.06 64	93.84 100	86.45 58	93.73 115	73.22 186	98.66 11	97.69 9

v8		86.22 104	86.83 98	84.36 155	87.82 211	62.35 245	86.42 124	91.33 133	76.78 128	92.73 53	94.48 63	73.41 199	93.72 116	83.10 73	95.41 150	97.01 22

Vis-MVSNet		86.86 92	86.58 100	87.72 93	92.09 110	77.43 106	87.35 106	92.09 111	78.87 106	84.27 216	94.05 87	78.35 145	93.65 117	80.54 105	91.58 244	92.08 182
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1240		84.30 140	84.51 142	83.65 172	87.65 216	61.26 255	82.85 199	91.54 126	67.94 240	90.68 88	90.65 191	71.71 219	93.64 118	82.84 78	94.78 174	96.07 36

TEST9					92.34 100	79.70 77	83.94 163	90.32 160	65.41 266	84.49 206	90.97 178	82.03 106	93.63 119

train_agg		85.98 109	85.28 124	88.07 90	92.34 100	79.70 77	83.94 163	90.32 160	65.79 256	84.49 206	90.97 178	81.93 108	93.63 119	81.21 94	96.54 105	90.88 212

PCF-MVS	74.62 15	82.15 180	80.92 196	85.84 128	89.43 180	72.30 154	80.53 241	91.82 120	57.36 315	87.81 145	89.92 207	77.67 151	93.63 119	58.69 295	95.08 164	91.58 199
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1192		84.57 132	84.69 137	84.21 159	87.75 213	62.88 235	83.02 194	91.43 129	69.08 226	89.98 101	90.89 182	72.70 209	93.62 122	82.41 82	94.97 168	96.13 34

v1921920		84.23 144	84.37 146	83.79 168	87.64 217	61.71 250	82.91 197	91.20 137	67.94 240	90.06 96	90.34 196	72.04 216	93.59 123	82.32 84	94.91 169	96.07 36

mvs_tets		89.78 52	89.27 63	91.30 28	93.51 69	84.79 41	89.89 62	90.63 152	70.00 219	94.55 15	96.67 11	87.94 39	93.59 123	84.27 63	95.97 129	95.52 49

test_0402		88.65 71	89.58 59	85.88 127	92.55 93	72.22 156	84.01 161	89.44 184	88.63 18	94.38 18	95.77 26	86.38 60	93.59 123	79.84 110	95.21 158	91.82 192

thisisatest0530		79.07 219	77.33 238	84.26 158	87.13 226	64.58 217	83.66 175	75.95 307	68.86 229	85.22 194	87.36 248	38.10 361	93.57 126	75.47 163	94.28 186	94.62 75

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 73	84.72 42	89.70 64	90.29 166	69.27 223	94.39 17	96.38 15	86.02 64	93.52 127	83.96 65	95.92 134	95.34 53

v144192		84.24 143	84.41 144	83.71 171	87.59 218	61.57 251	82.95 196	91.03 141	67.82 243	89.80 106	90.49 194	73.28 202	93.51 128	81.88 90	94.89 171	96.04 38

v1144		84.54 135	84.72 135	84.00 163	87.67 215	62.55 241	82.97 195	90.93 145	70.32 215	89.80 106	90.99 177	73.50 196	93.48 129	81.69 91	94.65 179	95.97 39

MCST-MVS		84.36 137	83.93 153	85.63 132	91.59 127	71.58 166	83.52 177	92.13 110	61.82 284	83.96 220	89.75 210	79.93 135	93.46 130	78.33 127	94.34 185	91.87 190

test_8					92.09 110	78.87 86	83.82 168	90.31 162	65.79 256	84.36 209	90.96 180	81.93 108	93.44 131

ACMH+	77.89 11	90.73 30	91.50 23	88.44 83	93.00 82	76.26 123	89.65 68	95.55 7	87.72 23	93.89 27	94.94 46	91.62 4	93.44 131	78.35 126	98.76 4	95.61 48

FC-MVSNet-test		85.93 110	87.05 93	82.58 196	92.25 104	56.44 303	85.75 133	93.09 82	77.33 122	91.94 68	94.65 55	74.78 182	93.41 133	75.11 168	98.58 14	97.88 7

OMC-MVS		88.19 75	87.52 86	90.19 49	91.94 116	81.68 63	87.49 105	93.17 79	76.02 136	88.64 130	91.22 168	84.24 76	93.37 134	77.97 136	97.03 88	95.52 49

MG-MVS		80.32 209	80.94 195	78.47 259	88.18 205	52.62 329	82.29 215	85.01 254	72.01 198	79.24 283	92.54 135	69.36 228	93.36 135	70.65 209	89.19 277	89.45 237

CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 34	86.95 15	92.09 29	92.30 107	79.74 92	87.50 149	92.38 138	81.42 117	93.28 136	83.07 74	97.24 83	91.67 196

F-COLMAP		84.97 127	83.42 157	89.63 57	92.39 98	83.40 50	88.83 86	91.92 117	73.19 178	80.18 275	89.15 220	77.04 160	93.28 136	65.82 250	92.28 229	92.21 179

v2v482		84.09 147	84.24 148	83.62 173	87.13 226	61.40 252	82.71 202	89.71 178	72.19 195	89.55 116	91.41 165	70.70 224	93.20 138	81.02 96	93.76 195	96.25 32

agg_prior1		85.72 112	85.20 125	87.28 99	91.58 130	77.69 99	83.69 173	90.30 163	66.29 253	84.32 210	91.07 175	82.13 102	93.18 139	81.02 96	96.36 113	90.98 208

agg_prior					91.58 130	77.69 99		90.30 163		84.32 210			93.18 139

Regformer-2		86.74 95	86.08 109	88.73 74	84.18 276	79.20 82	83.52 177	89.33 186	83.33 51	89.92 103	85.07 287	83.23 87	93.16 141	83.39 70	92.72 221	93.83 108

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 23	93.73 66	85.72 33	96.79 1	95.51 8	88.86 14	95.63 8	96.99 8	84.81 70	93.16 141	91.10 1	97.53 74	96.58 30
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
IterMVS-SCA-FT		80.64 201	79.41 215	84.34 156	83.93 280	69.66 178	76.28 301	81.09 280	72.43 186	86.47 175	90.19 201	60.46 271	93.15 143	77.45 142	86.39 306	90.22 228

DPM-MVS		80.10 215	79.18 218	82.88 191	90.71 159	69.74 176	78.87 266	90.84 146	60.29 299	75.64 311	85.92 270	67.28 238	93.11 144	71.24 202	91.79 239	85.77 285

XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 44	94.91 38	84.50 46	89.49 74	93.98 42	79.68 93	92.09 63	93.89 99	83.80 80	93.10 145	82.67 79	98.04 37	93.64 121

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 176	86.67 17	90.51 49	90.20 169	69.87 220	95.06 11	96.14 21	84.28 75	93.07 146	87.68 13	96.34 114	97.09 20

PC_three_1452									58.96 303	90.06 96	91.33 166	80.66 126	93.03 147	75.78 160	95.94 132	92.48 163

ACMM	79.39 9	90.65 31	90.99 40	89.63 57	95.03 35	83.53 49	89.62 69	93.35 66	79.20 101	93.83 28	93.60 108	90.81 8	92.96 148	85.02 55	98.45 19	92.41 166
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		83.18 167	82.64 170	84.79 145	89.05 187	67.82 197	77.93 277	92.52 102	68.33 234	85.07 196	81.54 325	82.06 104	92.96 148	69.35 219	97.91 50	93.57 124
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+		83.90 154	84.01 151	83.57 175	87.22 224	65.61 211	86.55 123	92.40 104	78.64 110	81.34 260	84.18 298	83.65 82	92.93 150	74.22 172	87.87 293	92.17 181

lessismore_v0				85.95 124	91.10 149	70.99 170		70.91 343		91.79 69	94.42 67	61.76 266	92.93 150	79.52 117	93.03 212	93.93 104

FIs		85.35 117	86.27 105	82.60 195	91.86 119	57.31 296	85.10 141	93.05 84	75.83 141	91.02 82	93.97 91	73.57 195	92.91 152	73.97 176	98.02 40	97.58 12

PVSNet_Blended_VisFu		81.55 188	80.49 200	84.70 149	91.58 130	73.24 139	84.21 155	91.67 124	62.86 277	80.94 262	87.16 252	67.27 239	92.87 153	69.82 216	88.94 280	87.99 260

Regformer-4		86.41 99	85.71 117	88.52 79	84.27 272	77.57 101	84.07 158	88.00 207	82.82 58	89.84 105	85.48 275	82.06 104	92.77 154	83.83 68	91.04 250	95.22 61

DELS-MVS		81.44 189	81.25 189	82.03 203	84.27 272	62.87 236	76.47 299	92.49 103	70.97 207	81.64 255	83.83 300	75.03 177	92.70 155	74.29 171	92.22 232	90.51 224
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
TSAR-MVS + GP.		83.95 152	82.69 169	87.72 93	89.27 184	81.45 65	83.72 172	81.58 278	74.73 155	85.66 187	86.06 267	72.56 211	92.69 156	75.44 164	95.21 158	89.01 251

Fast-Effi-MVS+		81.04 194	80.57 197	82.46 200	87.50 219	63.22 231	78.37 273	89.63 180	68.01 237	81.87 248	82.08 320	82.31 97	92.65 157	67.10 238	88.30 289	91.51 201

PLC	73.85 16	82.09 181	80.31 202	87.45 97	90.86 155	80.29 72	85.88 131	90.65 151	68.17 236	76.32 302	86.33 262	73.12 204	92.61 158	61.40 282	90.02 269	89.44 238
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
IterMVS-LS		84.73 129	84.98 129	83.96 165	87.35 221	63.66 225	83.25 187	89.88 176	76.06 134	89.62 112	92.37 141	73.40 201	92.52 159	78.16 131	94.77 176	95.69 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PAPM_NR		83.23 166	83.19 162	83.33 178	90.90 153	65.98 208	88.19 95	90.78 148	78.13 116	80.87 264	87.92 239	73.49 198	92.42 160	70.07 214	88.40 284	91.60 198

hse-mvs2		83.47 162	81.81 182	88.47 81	91.03 150	82.27 59	82.61 203	83.69 261	71.27 202	86.70 165	86.05 268	63.04 261	92.41 161	78.26 129	93.62 201	90.71 216

AUN-MVS		81.18 192	78.78 221	88.39 84	90.93 152	82.14 60	82.51 209	83.67 262	64.69 270	80.29 272	85.91 271	51.07 317	92.38 162	76.29 155	93.63 200	90.65 220

GeoE		85.45 116	85.81 114	84.37 153	90.08 170	67.07 200	85.86 132	91.39 132	72.33 191	87.59 147	90.25 199	84.85 69	92.37 163	78.00 134	91.94 238	93.66 118

PAPM		71.77 292	70.06 302	76.92 281	86.39 238	53.97 317	76.62 296	86.62 229	53.44 332	63.97 360	84.73 293	57.79 294	92.34 164	39.65 365	81.33 341	84.45 297

eth_miper_zixun_eth		80.84 197	80.22 206	82.71 193	81.41 302	60.98 261	77.81 279	90.14 171	67.31 246	86.95 161	87.24 251	64.26 251	92.31 165	75.23 166	91.61 242	94.85 71

PAPR		78.84 223	78.10 231	81.07 220	85.17 258	60.22 269	82.21 219	90.57 154	62.51 279	75.32 314	84.61 294	74.99 178	92.30 166	59.48 293	88.04 291	90.68 218

V42		83.47 162	83.37 159	83.75 170	83.16 288	63.33 229	81.31 231	90.23 168	69.51 222	90.91 85	90.81 185	74.16 188	92.29 167	80.06 107	90.22 267	95.62 47

QAPM		82.59 173	82.59 172	82.58 196	86.44 237	66.69 204	89.94 61	90.36 159	67.97 239	84.94 199	92.58 133	72.71 208	92.18 168	70.63 210	87.73 295	88.85 252

CSCG		86.26 102	86.47 101	85.60 133	90.87 154	74.26 132	87.98 97	91.85 118	80.35 85	89.54 118	88.01 235	79.09 138	92.13 169	75.51 162	95.06 165	90.41 226

TAPA-MVS	77.73 12	85.71 113	84.83 132	88.37 85	88.78 194	79.72 76	87.15 110	93.50 62	69.17 224	85.80 186	89.56 212	80.76 124	92.13 169	73.21 191	95.51 148	93.25 133
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0515		73.00 283	70.52 297	80.46 231	81.45 301	59.90 272	73.16 326	74.31 319	57.86 310	76.08 306	77.78 347	37.60 364	92.12 171	65.00 254	91.45 245	89.35 240

HyFIR lowres test		75.12 263	72.66 282	82.50 199	91.44 139	65.19 213	72.47 327	87.31 214	46.79 357	80.29 272	84.30 297	52.70 312	92.10 172	51.88 336	86.73 302	90.22 228

Anonymous20231211		88.40 73	89.62 58	84.73 147	90.46 164	65.27 212	88.86 85	93.02 88	87.15 25	93.05 44	97.10 6	82.28 100	92.02 173	76.70 149	97.99 42	96.88 24

baseline		85.20 119	85.93 111	83.02 185	86.30 244	62.37 244	84.55 149	93.96 43	74.48 159	87.12 153	92.03 148	82.30 98	91.94 174	78.39 124	94.21 187	94.74 74

EI-MVSNet-Vis-set		85.12 121	84.53 141	86.88 103	84.01 279	72.76 141	83.91 166	85.18 248	80.44 83	88.75 128	85.49 274	80.08 132	91.92 175	82.02 86	90.85 259	95.97 39

EI-MVSNet-UG-set		85.04 124	84.44 143	86.85 104	83.87 282	72.52 150	83.82 168	85.15 249	80.27 87	88.75 128	85.45 278	79.95 134	91.90 176	81.92 89	90.80 260	96.13 34

Regformer-1		86.00 107	85.50 121	87.49 96	84.18 276	76.90 114	83.52 177	87.94 209	82.18 65	89.19 121	85.07 287	82.28 100	91.89 177	82.40 83	92.72 221	93.69 117

casdiffmvs		85.21 118	85.85 113	83.31 179	86.17 249	62.77 237	83.03 193	93.93 44	74.69 156	88.21 140	92.68 130	82.29 99	91.89 177	77.87 137	93.75 197	95.27 57

IB-MVS	62.13 19	71.64 293	68.97 307	79.66 243	80.80 312	62.26 247	73.94 319	76.90 301	63.27 274	68.63 341	76.79 353	33.83 368	91.84 179	59.28 294	87.26 298	84.88 293
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
UGNet		82.78 170	81.64 184	86.21 120	86.20 248	76.24 124	86.86 113	85.68 240	77.07 125	73.76 322	92.82 124	69.64 226	91.82 180	69.04 225	93.69 198	90.56 222
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
BH-untuned		80.96 195	80.99 194	80.84 225	88.55 198	68.23 191	80.33 244	88.46 195	72.79 183	86.55 169	86.76 257	74.72 184	91.77 181	61.79 277	88.99 278	82.52 324

c3_l		81.64 187	81.59 186	81.79 211	80.86 310	59.15 281	78.61 270	90.18 170	68.36 233	87.20 151	87.11 254	69.39 227	91.62 182	78.16 131	94.43 184	94.60 76

API-MVS		82.28 177	82.61 171	81.30 215	86.29 245	69.79 175	88.71 89	87.67 211	78.42 113	82.15 244	84.15 299	77.98 147	91.59 183	65.39 252	92.75 218	82.51 325

nrg030		87.85 82	88.49 74	85.91 125	90.07 172	69.73 177	87.86 100	94.20 28	74.04 162	92.70 54	94.66 54	85.88 65	91.50 184	79.72 112	97.32 81	96.50 31

AllTest		87.97 80	87.40 89	89.68 55	91.59 127	83.40 50	89.50 73	95.44 9	79.47 95	88.00 142	93.03 115	82.66 92	91.47 185	70.81 204	96.14 123	94.16 95

TestCases				89.68 55	91.59 127	83.40 50		95.44 9	79.47 95	88.00 142	93.03 115	82.66 92	91.47 185	70.81 204	96.14 123	94.16 95

PVSNet_BlendedMVS		78.80 225	77.84 232	81.65 212	84.43 266	63.41 227	79.49 256	90.44 156	61.70 287	75.43 312	87.07 255	69.11 230	91.44 187	60.68 287	92.24 230	90.11 232

PVSNet_Blended		76.49 252	75.40 256	79.76 240	84.43 266	63.41 227	75.14 312	90.44 156	57.36 315	75.43 312	78.30 346	69.11 230	91.44 187	60.68 287	87.70 296	84.42 298

miper_ehance_all_eth		80.34 208	80.04 211	81.24 218	79.82 321	58.95 283	77.66 281	89.66 179	65.75 259	85.99 184	85.11 283	68.29 234	91.42 189	76.03 157	92.03 234	93.33 128

无先验							82.81 200	85.62 241	58.09 308				91.41 190	67.95 236		84.48 296

1121		80.86 196	79.81 213	84.02 162	93.93 60	78.70 88	81.64 226	80.18 286	55.43 323	83.67 223	91.15 171	71.29 221	91.41 190	67.95 236	93.06 211	81.96 330

ambc				82.98 186	90.55 163	64.86 215	88.20 94	89.15 188		89.40 119	93.96 94	71.67 220	91.38 192	78.83 122	96.55 104	92.71 154

UniMVSNet_ETH3D		89.12 64	90.72 46	84.31 157	97.00 2	64.33 221	89.67 67	88.38 198	88.84 15	94.29 19	97.57 3	90.48 14	91.26 193	72.57 195	97.65 63	97.34 15

miper_enhance_ethall		77.83 235	76.93 242	80.51 230	76.15 348	58.01 291	75.47 310	88.82 190	58.05 309	83.59 225	80.69 329	64.41 250	91.20 194	73.16 192	92.03 234	92.33 171

3Dnovator	80.37 7	84.80 128	84.71 136	85.06 142	86.36 242	74.71 129	88.77 88	90.00 174	75.65 144	84.96 197	93.17 113	74.06 189	91.19 195	78.28 128	91.09 248	89.29 243

cascas		76.29 255	74.81 260	80.72 228	84.47 265	62.94 234	73.89 320	87.34 213	55.94 320	75.16 316	76.53 355	63.97 253	91.16 196	65.00 254	90.97 255	88.06 258

ET-MVSNet_ETH3D		75.28 260	72.77 280	82.81 192	83.03 291	68.11 193	77.09 289	76.51 305	60.67 297	77.60 296	80.52 333	38.04 362	91.15 197	70.78 206	90.68 262	89.17 244

EG-PatchMatch MVS		84.08 148	84.11 149	83.98 164	92.22 106	72.61 147	82.20 221	87.02 224	72.63 185	88.86 125	91.02 176	78.52 141	91.11 198	73.41 184	91.09 248	88.21 256

WR-MVS		83.56 159	84.40 145	81.06 221	93.43 72	54.88 314	78.67 269	85.02 253	81.24 75	90.74 87	91.56 162	72.85 206	91.08 199	68.00 234	98.04 37	97.23 17

canonicalmvs		85.50 114	86.14 108	83.58 174	87.97 208	67.13 199	87.55 103	94.32 22	73.44 170	88.47 134	87.54 244	86.45 58	91.06 200	75.76 161	93.76 195	92.54 161

XVG-OURS		89.18 62	88.83 71	90.23 48	94.28 48	86.11 25	85.91 129	93.60 60	80.16 88	89.13 123	93.44 109	83.82 79	90.98 201	83.86 67	95.30 157	93.60 123

PS-MVSNAJ		77.04 244	76.53 246	78.56 256	87.09 231	61.40 252	75.26 311	87.13 219	61.25 289	74.38 320	77.22 352	76.94 162	90.94 202	64.63 259	84.83 322	83.35 313

xiu_mvs_v2_base		77.19 242	76.75 244	78.52 257	87.01 232	61.30 254	75.55 309	87.12 222	61.24 290	74.45 318	78.79 343	77.20 156	90.93 203	64.62 260	84.80 323	83.32 314

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 47	94.47 46	85.95 26	86.84 114	93.91 45	80.07 90	86.75 164	93.26 111	93.64 2	90.93 203	84.60 60	90.75 261	93.97 102

v148		82.31 176	82.48 174	81.81 210	85.59 254	59.66 274	81.47 229	86.02 236	72.85 182	88.05 141	90.65 191	70.73 223	90.91 205	75.15 167	91.79 239	94.87 67

VDD-MVS		84.23 144	84.58 140	83.20 182	91.17 147	65.16 214	83.25 187	84.97 256	79.79 91	87.18 152	94.27 73	74.77 183	90.89 206	69.24 220	96.54 105	93.55 127

cl22		78.97 220	78.21 230	81.24 218	77.74 335	59.01 282	77.46 287	87.13 219	65.79 256	84.32 210	85.10 284	58.96 285	90.88 207	75.36 165	92.03 234	93.84 107

alignmvs		83.94 153	83.98 152	83.80 167	87.80 212	67.88 196	84.54 151	91.42 131	73.27 177	88.41 136	87.96 236	72.33 212	90.83 208	76.02 158	94.11 189	92.69 155

ITE_SJBPF				90.11 50	90.72 158	84.97 38		90.30 163	81.56 72	90.02 98	91.20 170	82.40 96	90.81 209	73.58 182	94.66 178	94.56 77

BH-RMVSNet		80.53 202	80.22 206	81.49 214	87.19 225	66.21 207	77.79 280	86.23 233	74.21 161	83.69 222	88.50 229	73.25 203	90.75 210	63.18 268	87.90 292	87.52 266

BH-w/o		76.57 250	76.07 251	78.10 266	86.88 235	65.92 209	77.63 282	86.33 231	65.69 260	80.89 263	79.95 338	68.97 232	90.74 211	53.01 329	85.25 315	77.62 350

TR-MVS		76.77 248	75.79 252	79.72 241	86.10 251	65.79 210	77.14 288	83.02 266	65.20 267	81.40 258	82.10 319	66.30 243	90.73 212	55.57 312	85.27 314	82.65 320

GBi-Net		82.02 182	82.07 178	81.85 207	86.38 239	61.05 258	86.83 115	88.27 202	72.43 186	86.00 181	95.64 31	63.78 255	90.68 213	65.95 246	93.34 203	93.82 110

test1		82.02 182	82.07 178	81.85 207	86.38 239	61.05 258	86.83 115	88.27 202	72.43 186	86.00 181	95.64 31	63.78 255	90.68 213	65.95 246	93.34 203	93.82 110

FMVSNet1		84.55 133	85.45 122	81.85 207	90.27 167	61.05 258	86.83 115	88.27 202	78.57 111	89.66 111	95.64 31	75.43 173	90.68 213	69.09 224	95.33 153	93.82 110

VDDNet		84.35 138	85.39 123	81.25 216	95.13 33	59.32 277	85.42 138	81.11 279	86.41 29	87.41 150	96.21 19	73.61 194	90.61 216	66.33 244	96.85 93	93.81 113

MAR-MVS		80.24 211	78.74 223	84.73 147	86.87 236	78.18 92	85.75 133	87.81 210	65.67 261	77.84 292	78.50 345	73.79 193	90.53 217	61.59 281	90.87 258	85.49 288
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
MVS_Test		82.47 175	83.22 160	80.22 235	82.62 294	57.75 294	82.54 208	91.96 116	71.16 206	82.89 234	92.52 136	77.41 154	90.50 218	80.04 108	87.84 294	92.40 167

MVS_111021_HR		84.63 130	84.34 147	85.49 136	90.18 169	75.86 125	79.23 262	87.13 219	73.35 171	85.56 190	89.34 215	83.60 83	90.50 218	76.64 150	94.05 191	90.09 233

Anonymous20240529		86.20 105	87.13 90	83.42 177	90.19 168	64.55 219	84.55 149	90.71 149	85.85 31	89.94 102	95.24 40	82.13 102	90.40 220	69.19 223	96.40 112	95.31 55

Regformer-3		85.06 123	84.67 138	86.22 118	84.27 272	73.43 136	84.07 158	85.26 246	80.77 82	88.62 131	85.48 275	80.56 128	90.39 221	81.99 87	91.04 250	94.85 71

EI-MVSNet		82.61 172	82.42 175	83.20 182	83.25 286	63.66 225	83.50 180	85.07 250	76.06 134	86.55 169	85.10 284	73.41 199	90.25 222	78.15 133	90.67 263	95.68 45

MVSTER		77.09 243	75.70 254	81.25 216	75.27 355	61.08 257	77.49 286	85.07 250	60.78 295	86.55 169	88.68 227	43.14 353	90.25 222	73.69 181	90.67 263	92.42 165

Fast-Effi-MVS+-dtu		82.54 174	81.41 188	85.90 126	85.60 253	76.53 120	83.07 192	89.62 181	73.02 181	79.11 284	83.51 303	80.74 125	90.24 224	68.76 227	89.29 274	90.94 210

SD-MVS		88.96 67	89.88 53	86.22 118	91.63 126	77.07 112	89.82 63	93.77 51	78.90 105	92.88 46	92.29 143	86.11 62	90.22 225	86.24 42	97.24 83	91.36 203
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
FMVSNet2		81.31 190	81.61 185	80.41 232	86.38 239	58.75 288	83.93 165	86.58 230	72.43 186	87.65 146	92.98 117	63.78 255	90.22 225	66.86 239	93.92 193	92.27 176

cl____		80.42 205	80.23 204	81.02 222	79.99 319	59.25 278	77.07 290	87.02 224	67.37 245	86.18 178	89.21 218	63.08 260	90.16 227	76.31 154	95.80 140	93.65 120

DIV-MVS_self_test		80.43 204	80.23 204	81.02 222	79.99 319	59.25 278	77.07 290	87.02 224	67.38 244	86.19 176	89.22 217	63.09 259	90.16 227	76.32 153	95.80 140	93.66 118

OpenMVS	76.72 13	81.98 184	82.00 180	81.93 204	84.42 268	68.22 192	88.50 93	89.48 183	66.92 248	81.80 252	91.86 151	72.59 210	90.16 227	71.19 203	91.25 247	87.40 268

xiu_mvs_v1_base_debu		80.84 197	80.14 208	82.93 188	88.31 202	71.73 162	79.53 253	87.17 216	65.43 262	79.59 277	82.73 315	76.94 162	90.14 230	73.22 186	88.33 285	86.90 274

xiu_mvs_v1_base		80.84 197	80.14 208	82.93 188	88.31 202	71.73 162	79.53 253	87.17 216	65.43 262	79.59 277	82.73 315	76.94 162	90.14 230	73.22 186	88.33 285	86.90 274

xiu_mvs_v1_base_debi		80.84 197	80.14 208	82.93 188	88.31 202	71.73 162	79.53 253	87.17 216	65.43 262	79.59 277	82.73 315	76.94 162	90.14 230	73.22 186	88.33 285	86.90 274

FMVSNet3		78.80 225	78.55 225	79.57 244	82.89 293	56.89 301	81.76 223	85.77 239	69.04 227	86.00 181	90.44 195	51.75 315	90.09 233	65.95 246	93.34 203	91.72 194

test1111		78.53 229	78.85 220	77.56 274	92.22 106	47.49 355	82.61 203	69.24 349	72.43 186	85.28 193	94.20 79	51.91 313	90.07 234	65.36 253	96.45 110	95.11 62

LFMVS		80.15 214	80.56 198	78.89 250	89.19 186	55.93 305	85.22 140	73.78 324	82.96 56	84.28 215	92.72 128	57.38 295	90.07 234	63.80 262	95.75 143	90.68 218

test_yl		78.71 227	78.51 226	79.32 247	84.32 270	58.84 285	78.38 271	85.33 244	75.99 137	82.49 237	86.57 258	58.01 289	90.02 236	62.74 269	92.73 219	89.10 246

DCV-MVSNet		78.71 227	78.51 226	79.32 247	84.32 270	58.84 285	78.38 271	85.33 244	75.99 137	82.49 237	86.57 258	58.01 289	90.02 236	62.74 269	92.73 219	89.10 246

ECVR-MVS		78.44 230	78.63 224	77.88 270	91.85 120	48.95 349	83.68 174	69.91 347	72.30 193	84.26 217	94.20 79	51.89 314	89.82 238	63.58 263	96.02 127	94.87 67

test2506		74.12 274	73.39 274	76.28 290	91.85 120	44.20 365	84.06 160	48.20 377	72.30 193	81.90 247	94.20 79	27.22 378	89.77 239	64.81 256	96.02 127	94.87 67

MVS		73.21 281	72.59 283	75.06 298	80.97 307	60.81 264	81.64 226	85.92 238	46.03 360	71.68 331	77.54 348	68.47 233	89.77 239	55.70 311	85.39 312	74.60 355

LCM-MVSNet-Re		83.48 161	85.06 127	78.75 253	85.94 252	55.75 308	80.05 246	94.27 23	76.47 129	96.09 5	94.54 60	83.31 86	89.75 241	59.95 290	94.89 171	90.75 215

EGC-MVSNET		74.79 269	69.99 303	89.19 66	94.89 39	87.00 14	91.89 35	86.28 232	1.09 375	2.23 377	95.98 24	81.87 112	89.48 242	79.76 111	95.96 130	91.10 206

CANet_DTU		77.81 237	77.05 240	80.09 237	81.37 303	59.90 272	83.26 186	88.29 201	69.16 225	67.83 345	83.72 301	60.93 268	89.47 243	69.22 222	89.70 271	90.88 212

GA-MVS		75.83 257	74.61 261	79.48 246	81.87 297	59.25 278	73.42 323	82.88 267	68.68 231	79.75 276	81.80 322	50.62 319	89.46 244	66.85 240	85.64 311	89.72 234

MVP-Stereo		75.81 258	73.51 273	82.71 193	89.35 181	73.62 134	80.06 245	85.20 247	60.30 298	73.96 321	87.94 237	57.89 293	89.45 245	52.02 332	74.87 359	85.06 292
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Vis-MVSNet (Re-imp)		77.82 236	77.79 233	77.92 269	88.82 193	51.29 339	83.28 185	71.97 337	74.04 162	82.23 242	89.78 209	57.38 295	89.41 246	57.22 303	95.41 150	93.05 139

MSLP-MVS++		85.00 126	86.03 110	81.90 205	91.84 122	71.56 167	86.75 119	93.02 88	75.95 139	87.12 153	89.39 214	77.98 147	89.40 247	77.46 141	94.78 174	84.75 295

thres600view7		75.97 256	75.35 258	77.85 272	87.01 232	51.84 335	80.45 242	73.26 328	75.20 150	83.10 232	86.31 264	45.54 338	89.05 248	55.03 318	92.24 230	92.66 156

jason		77.42 240	75.75 253	82.43 201	87.10 230	69.27 182	77.99 276	81.94 275	51.47 345	77.84 292	85.07 287	60.32 273	89.00 249	70.74 208	89.27 276	89.03 249
jason: jason.
lupinMVS		76.37 254	74.46 264	82.09 202	85.54 255	69.26 183	76.79 292	80.77 283	50.68 351	76.23 303	82.82 313	58.69 286	88.94 250	69.85 215	88.77 281	88.07 257

PMVS	80.48 6	90.08 41	90.66 47	88.34 86	96.71 3	92.97 1	90.31 53	89.57 182	88.51 19	90.11 95	95.12 43	90.98 7	88.92 251	77.55 140	97.07 87	83.13 318
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
thres100view900		75.45 259	75.05 259	76.66 286	87.27 222	51.88 334	81.07 236	73.26 328	75.68 143	83.25 229	86.37 261	45.54 338	88.80 252	51.98 333	90.99 252	89.31 241

tfpn200view9		74.86 267	74.23 266	76.74 285	86.24 246	52.12 331	79.24 260	73.87 322	73.34 172	81.82 250	84.60 295	46.02 332	88.80 252	51.98 333	90.99 252	89.31 241

thres400		75.14 261	74.23 266	77.86 271	86.24 246	52.12 331	79.24 260	73.87 322	73.34 172	81.82 250	84.60 295	46.02 332	88.80 252	51.98 333	90.99 252	92.66 156

TAMVS		78.08 234	76.36 247	83.23 180	90.62 160	72.87 140	79.08 263	80.01 288	61.72 286	81.35 259	86.92 256	63.96 254	88.78 255	50.61 337	93.01 213	88.04 259

CDS-MVSNet		77.32 241	75.40 256	83.06 184	89.00 189	72.48 151	77.90 278	82.17 273	60.81 294	78.94 285	83.49 304	59.30 281	88.76 256	54.64 321	92.37 225	87.93 262
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OpenMVS_ROB	70.19 17	77.77 238	77.46 234	78.71 254	84.39 269	61.15 256	81.18 235	82.52 269	62.45 281	83.34 228	87.37 247	66.20 244	88.66 257	64.69 258	85.02 317	86.32 278

baseline2		69.77 306	66.89 316	78.41 260	79.51 324	58.09 290	76.23 302	69.57 348	57.50 314	64.82 358	77.45 350	46.02 332	88.44 258	53.08 326	77.83 352	88.70 253

tpm2		68.45 311	66.83 317	73.30 305	78.93 332	48.50 350	79.76 250	71.76 339	47.50 356	69.92 338	83.60 302	42.07 355	88.40 259	48.44 347	79.51 345	83.01 319

新几何1				82.95 187	93.96 59	78.56 90		80.24 285	55.45 322	83.93 221	91.08 173	71.19 222	88.33 260	65.84 249	93.07 210	81.95 331

ACMH	76.49 14	89.34 60	91.14 34	83.96 165	92.50 96	70.36 174	89.55 70	93.84 50	81.89 69	94.70 13	95.44 35	90.69 9	88.31 261	83.33 72	98.30 26	93.20 134
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres200		72.34 288	71.55 293	74.70 300	83.48 283	51.60 336	75.02 313	73.71 325	70.14 218	78.56 288	80.57 332	46.20 330	88.20 262	46.99 352	89.29 274	84.32 299

gm-plane-assit					75.42 354	44.97 364			52.17 339		72.36 362		87.90 263	54.10 322

EU-MVSNet		75.12 263	74.43 265	77.18 278	83.11 290	59.48 276	85.71 135	82.43 271	39.76 369	85.64 188	88.76 225	44.71 349	87.88 264	73.86 178	85.88 310	84.16 301

RPSCF		88.00 79	86.93 96	91.22 31	90.08 170	89.30 5	89.68 66	91.11 139	79.26 100	89.68 109	94.81 53	82.44 94	87.74 265	76.54 152	88.74 283	96.61 29

D2MVS		76.84 246	75.67 255	80.34 233	80.48 317	62.16 248	73.50 322	84.80 258	57.61 313	82.24 241	87.54 244	51.31 316	87.65 266	70.40 213	93.19 208	91.23 204

dcpmvs_2		84.23 144	85.14 126	81.50 213	88.61 196	61.98 249	82.90 198	93.11 80	68.66 232	92.77 52	92.39 137	78.50 142	87.63 267	76.99 148	92.30 226	94.90 65

CostFormer		69.98 305	68.68 310	73.87 302	77.14 339	50.72 344	79.26 259	74.51 317	51.94 343	70.97 335	84.75 292	45.16 346	87.49 268	55.16 317	79.23 348	83.40 312

CVMVSNet		72.62 285	71.41 294	76.28 290	83.25 286	60.34 268	83.50 180	79.02 293	37.77 370	76.33 301	85.10 284	49.60 322	87.41 269	70.54 211	77.54 355	81.08 342

diffmvs		80.40 206	80.48 201	80.17 236	79.02 331	60.04 270	77.54 284	90.28 167	66.65 251	82.40 239	87.33 249	73.50 196	87.35 270	77.98 135	89.62 272	93.13 136

VPA-MVSNet		83.47 162	84.73 133	79.69 242	90.29 166	57.52 295	81.30 233	88.69 193	76.29 130	87.58 148	94.44 64	80.60 127	87.20 271	66.60 243	96.82 96	94.34 89

patchmatchnet-post											81.71 323	45.93 335	87.01 272

SCA		73.32 278	72.57 284	75.58 295	81.62 299	55.86 306	78.89 265	71.37 342	61.73 285	74.93 317	83.42 306	60.46 271	87.01 272	58.11 300	82.63 337	83.88 302

mvs_anonymous		78.13 233	78.76 222	76.23 292	79.24 328	50.31 346	78.69 268	84.82 257	61.60 288	83.09 233	92.82 124	73.89 192	87.01 272	68.33 233	86.41 305	91.37 202

TinyColmap		81.25 191	82.34 176	77.99 268	85.33 257	60.68 266	82.32 214	88.33 200	71.26 204	86.97 160	92.22 147	77.10 159	86.98 275	62.37 271	95.17 160	86.31 279

TransMVSNet (Re)		84.02 150	85.74 116	78.85 251	91.00 151	55.20 313	82.29 215	87.26 215	79.65 94	88.38 137	95.52 34	83.00 88	86.88 276	67.97 235	96.60 103	94.45 84

LF4IMVS		82.75 171	81.93 181	85.19 138	82.08 295	80.15 73	85.53 136	88.76 192	68.01 237	85.58 189	87.75 240	71.80 218	86.85 277	74.02 175	93.87 194	88.58 254

pmmvs6		86.52 98	88.06 79	81.90 205	92.22 106	62.28 246	84.66 147	89.15 188	83.54 49	89.85 104	97.32 4	88.08 38	86.80 278	70.43 212	97.30 82	96.62 28

KD-MVS_self_test		81.93 185	83.14 163	78.30 262	84.75 263	52.75 326	80.37 243	89.42 185	70.24 217	90.26 94	93.39 110	74.55 187	86.77 279	68.61 230	96.64 100	95.38 52

MVS_0304		78.17 232	77.23 239	80.99 224	84.13 278	69.07 189	81.39 230	80.81 282	76.28 131	67.53 347	89.11 221	62.87 263	86.77 279	60.90 286	92.01 237	87.13 271

1112_ss		74.82 268	73.74 269	78.04 267	89.57 177	60.04 270	76.49 298	87.09 223	54.31 327	73.66 323	79.80 339	60.25 274	86.76 281	58.37 296	84.15 326	87.32 269

USDC		76.63 249	76.73 245	76.34 289	83.46 284	57.20 298	80.02 247	88.04 206	52.14 341	83.65 224	91.25 167	63.24 258	86.65 282	54.66 320	94.11 189	85.17 290

tfpnnormal		81.79 186	82.95 165	78.31 261	88.93 191	55.40 309	80.83 240	82.85 268	76.81 127	85.90 185	94.14 84	74.58 186	86.51 283	66.82 242	95.68 146	93.01 141

VPNet		80.25 210	81.68 183	75.94 293	92.46 97	47.98 353	76.70 294	81.67 277	73.45 169	84.87 200	92.82 124	74.66 185	86.51 283	61.66 279	96.85 93	93.33 128

testdata2													86.43 285	63.52 265

MSDG		80.06 216	79.99 212	80.25 234	83.91 281	68.04 195	77.51 285	89.19 187	77.65 119	81.94 246	83.45 305	76.37 170	86.31 286	63.31 267	86.59 303	86.41 277

Anonymous202405211		80.51 203	81.19 192	78.49 258	88.48 199	57.26 297	76.63 295	82.49 270	81.21 76	84.30 214	92.24 146	67.99 235	86.24 287	62.22 272	95.13 161	91.98 188

MVS_111021_LR		84.28 141	83.76 155	85.83 129	89.23 185	83.07 53	80.99 237	83.56 263	72.71 184	86.07 180	89.07 222	81.75 114	86.19 288	77.11 146	93.36 202	88.24 255

Baseline_NR-MVSNet		84.00 151	85.90 112	78.29 263	91.47 138	53.44 322	82.29 215	87.00 227	79.06 103	89.55 116	95.72 29	77.20 156	86.14 289	72.30 197	98.51 17	95.28 56

EPNet_dtu		72.87 284	71.33 295	77.49 276	77.72 336	60.55 267	82.35 213	75.79 308	66.49 252	58.39 370	81.06 328	53.68 310	85.98 290	53.55 324	92.97 215	85.95 282
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ANet_high		83.17 168	85.68 118	75.65 294	81.24 304	45.26 362	79.94 248	92.91 91	83.83 43	91.33 77	96.88 10	80.25 131	85.92 291	68.89 226	95.89 135	95.76 43

Test_1112_low_res		73.90 276	73.08 277	76.35 288	90.35 165	55.95 304	73.40 324	86.17 234	50.70 350	73.14 324	85.94 269	58.31 288	85.90 292	56.51 306	83.22 330	87.20 270

MIMVSNet1		83.63 158	84.59 139	80.74 226	94.06 57	62.77 237	82.72 201	84.53 259	77.57 121	90.34 92	95.92 25	76.88 168	85.83 293	61.88 276	97.42 78	93.62 122

tpmvs		70.16 301	69.56 305	71.96 314	74.71 359	48.13 351	79.63 251	75.45 313	65.02 268	70.26 336	81.88 321	45.34 343	85.68 294	58.34 297	75.39 358	82.08 329

pm-mvs1		83.69 156	84.95 130	79.91 238	90.04 174	59.66 274	82.43 211	87.44 212	75.52 145	87.85 144	95.26 39	81.25 119	85.65 295	68.74 228	96.04 126	94.42 85

pmmvs-eth3d		78.42 231	77.04 241	82.57 198	87.44 220	74.41 131	80.86 239	79.67 289	55.68 321	84.69 203	90.31 198	60.91 269	85.42 296	62.20 273	91.59 243	87.88 263

testdata				79.54 245	92.87 85	72.34 153		80.14 287	59.91 301	85.47 192	91.75 158	67.96 236	85.24 297	68.57 232	92.18 233	81.06 344

1314		73.22 280	72.56 285	75.20 296	80.41 318	57.84 292	81.64 226	85.36 243	51.68 344	73.10 325	76.65 354	61.45 267	85.19 298	63.54 264	79.21 349	82.59 321

CHOSEN 1792x2688		72.45 286	70.56 296	78.13 265	90.02 175	63.08 232	68.72 340	83.16 264	42.99 366	75.92 307	85.46 277	57.22 297	85.18 299	49.87 341	81.67 338	86.14 280

pmmvs4		74.92 266	72.98 279	80.73 227	84.95 259	71.71 165	76.23 302	77.59 298	52.83 335	77.73 295	86.38 260	56.35 301	84.97 300	57.72 302	87.05 300	85.51 287

旧先验2							81.73 224		56.88 318	86.54 174			84.90 301	72.81 193

DWT-MVSNet_test		66.43 319	64.37 325	72.63 309	74.86 358	50.86 343	76.52 297	72.74 331	54.06 329	65.50 352	68.30 366	32.13 370	84.84 302	61.63 280	73.59 360	82.19 327

HY-MVS	64.64 18	73.03 282	72.47 286	74.71 299	83.36 285	54.19 316	82.14 222	81.96 274	56.76 319	69.57 339	86.21 266	60.03 275	84.83 303	49.58 342	82.65 335	85.11 291

ab-mvs		79.67 217	80.56 198	76.99 279	88.48 199	56.93 299	84.70 146	86.06 235	68.95 228	80.78 265	93.08 114	75.30 175	84.62 304	56.78 304	90.90 257	89.43 239

IterMVS		76.91 245	76.34 248	78.64 255	80.91 308	64.03 223	76.30 300	79.03 292	64.88 269	83.11 231	89.16 219	59.90 277	84.46 305	68.61 230	85.15 316	87.42 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VNet		79.31 218	80.27 203	76.44 287	87.92 210	53.95 318	75.58 308	84.35 260	74.39 160	82.23 242	90.72 187	72.84 207	84.39 306	60.38 289	93.98 192	90.97 209

ppachtmachnet_test		74.73 270	74.00 268	76.90 282	80.71 313	56.89 301	71.53 331	78.42 294	58.24 307	79.32 282	82.92 312	57.91 292	84.26 307	65.60 251	91.36 246	89.56 236

CR-MVSNet		74.00 275	73.04 278	76.85 284	79.58 322	62.64 239	82.58 205	76.90 301	50.50 352	75.72 309	92.38 138	48.07 325	84.07 308	68.72 229	82.91 333	83.85 305

Patchmtry		76.56 251	77.46 234	73.83 303	79.37 327	46.60 359	82.41 212	76.90 301	73.81 165	85.56 190	92.38 138	48.07 325	83.98 309	63.36 266	95.31 156	90.92 211

gg-mvs-nofinetune		68.96 310	69.11 306	68.52 329	76.12 349	45.32 361	83.59 176	55.88 372	86.68 26	64.62 359	97.01 7	30.36 373	83.97 310	44.78 357	82.94 332	76.26 352

GG-mvs-BLEND				67.16 332	73.36 362	46.54 360	84.15 156	55.04 373		58.64 369	61.95 370	29.93 374	83.87 311	38.71 367	76.92 356	71.07 359

PM-MVS		80.20 212	79.00 219	83.78 169	88.17 206	86.66 18	81.31 231	66.81 357	69.64 221	88.33 138	90.19 201	64.58 249	83.63 312	71.99 200	90.03 268	81.06 344

JIA-IIPM		69.41 308	66.64 320	77.70 273	73.19 363	71.24 168	75.67 305	65.56 358	70.42 212	65.18 354	92.97 119	33.64 369	83.06 313	53.52 325	69.61 368	78.79 349

CMPMVS	59.41 20	75.12 263	73.57 271	79.77 239	75.84 350	67.22 198	81.21 234	82.18 272	50.78 349	76.50 299	87.66 242	55.20 307	82.99 314	62.17 275	90.64 266	89.09 248
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Patchmatch-RL test		74.48 271	73.68 270	76.89 283	84.83 261	66.54 205	72.29 328	69.16 350	57.70 311	86.76 163	86.33 262	45.79 337	82.59 315	69.63 217	90.65 265	81.54 335

KD-MVS_2432*1600		66.87 316	65.81 321	70.04 317	67.50 372	47.49 355	62.56 355	79.16 290	61.21 291	77.98 290	80.61 330	25.29 380	82.48 316	53.02 327	84.92 318	80.16 347

miper_refine_blended		66.87 316	65.81 321	70.04 317	67.50 372	47.49 355	62.56 355	79.16 290	61.21 291	77.98 290	80.61 330	25.29 380	82.48 316	53.02 327	84.92 318	80.16 347

tpm cat1		66.76 318	65.21 324	71.42 315	77.09 340	50.62 345	78.01 275	73.68 326	44.89 362	68.64 340	79.00 342	45.51 340	82.42 318	49.91 340	70.15 365	81.23 341

MS-PatchMatch		70.93 297	70.22 300	73.06 307	81.85 298	62.50 242	73.82 321	77.90 296	52.44 338	75.92 307	81.27 326	55.67 304	81.75 319	55.37 314	77.70 353	74.94 354

CNLPA		83.55 160	83.10 164	84.90 143	89.34 182	83.87 48	84.54 151	88.77 191	79.09 102	83.54 227	88.66 228	74.87 179	81.73 320	66.84 241	92.29 228	89.11 245

baseline1		73.26 279	73.54 272	72.43 312	84.92 260	47.79 354	79.89 249	74.00 320	65.93 254	78.81 286	86.28 265	56.36 300	81.63 321	56.63 305	79.04 350	87.87 264

MDA-MVSNet-bldmvs		77.47 239	76.90 243	79.16 249	79.03 330	64.59 216	66.58 348	75.67 310	73.15 179	88.86 125	88.99 223	66.94 240	81.23 322	64.71 257	88.22 290	91.64 197

CL-MVSNet_self_test		76.81 247	77.38 236	75.12 297	86.90 234	51.34 337	73.20 325	80.63 284	68.30 235	81.80 252	88.40 230	66.92 241	80.90 323	55.35 315	94.90 170	93.12 137

MDTV_nov1_ep13			68.29 312		78.03 334	43.87 366	74.12 318	72.22 335	52.17 339	67.02 348	85.54 273	45.36 342	80.85 324	55.73 309	84.42 325

pmmvs5		70.73 298	70.07 301	72.72 308	77.03 341	52.73 327	74.14 317	75.65 311	50.36 353	72.17 329	85.37 281	55.42 306	80.67 325	52.86 330	87.59 297	84.77 294

Gipuma		84.44 136	86.33 103	78.78 252	84.20 275	73.57 135	89.55 70	90.44 156	84.24 39	84.38 208	94.89 47	76.35 171	80.40 326	76.14 156	96.80 97	82.36 326
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_post1							78.85 267				3.13 375	45.19 345	80.13 327	58.11 300

PatchmatchNet		69.71 307	68.83 308	72.33 313	77.66 337	53.60 320	79.29 258	69.99 346	57.66 312	72.53 327	82.93 311	46.45 329	80.08 328	60.91 285	72.09 362	83.31 315
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FMVSNet5		72.10 290	71.69 290	73.32 304	81.57 300	53.02 325	76.77 293	78.37 295	63.31 273	76.37 300	91.85 152	36.68 365	78.98 329	47.87 349	92.45 224	87.95 261

our_test_3		71.85 291	71.59 291	72.62 310	80.71 313	53.78 319	69.72 338	71.71 341	58.80 304	78.03 289	80.51 334	56.61 299	78.84 330	62.20 273	86.04 309	85.23 289

miper_lstm_enhance		76.45 253	76.10 250	77.51 275	76.72 343	60.97 262	64.69 351	85.04 252	63.98 272	83.20 230	88.22 232	56.67 298	78.79 331	73.22 186	93.12 209	92.78 150

PatchMatch-RL		74.48 271	73.22 276	78.27 264	87.70 214	85.26 35	75.92 304	70.09 345	64.34 271	76.09 305	81.25 327	65.87 247	78.07 332	53.86 323	83.82 327	71.48 358

Anonymous20240521		80.18 213	81.25 189	76.95 280	83.15 289	60.84 263	82.46 210	85.99 237	68.76 230	86.78 162	93.73 106	59.13 283	77.44 333	73.71 180	97.55 70	92.56 159

ADS-MVSNet2		65.87 323	63.64 328	72.55 311	73.16 364	56.92 300	67.10 346	74.81 314	49.74 354	66.04 350	82.97 309	46.71 327	77.26 334	42.29 360	69.96 366	83.46 310

test_post											3.10 376	45.43 341	77.22 335

MVS-HIRNet		61.16 332	62.92 329	55.87 352	79.09 329	35.34 374	71.83 329	57.98 371	46.56 358	59.05 367	91.14 172	49.95 321	76.43 336	38.74 366	71.92 363	55.84 370

MIMVSNet		71.09 296	71.59 291	69.57 322	87.23 223	50.07 347	78.91 264	71.83 338	60.20 300	71.26 332	91.76 157	55.08 308	76.09 337	41.06 363	87.02 301	82.54 323

tpm		67.95 312	68.08 313	67.55 331	78.74 333	43.53 367	75.60 306	67.10 356	54.92 325	72.23 328	88.10 234	42.87 354	75.97 338	52.21 331	80.95 344	83.15 317

FPMVS		72.29 289	72.00 288	73.14 306	88.63 195	85.00 37	74.65 316	67.39 351	71.94 199	77.80 294	87.66 242	50.48 320	75.83 339	49.95 339	79.51 345	58.58 369

PatchT		70.52 299	72.76 281	63.79 341	79.38 326	33.53 375	77.63 282	65.37 359	73.61 167	71.77 330	92.79 127	44.38 350	75.65 340	64.53 261	85.37 313	82.18 328

PVSNet	58.17 21	66.41 320	65.63 323	68.75 326	81.96 296	49.88 348	62.19 357	72.51 334	51.03 347	68.04 343	75.34 358	50.84 318	74.77 341	45.82 356	82.96 331	81.60 334

tpmrst		66.28 321	66.69 319	65.05 339	72.82 367	39.33 370	78.20 274	70.69 344	53.16 334	67.88 344	80.36 335	48.18 324	74.75 342	58.13 299	70.79 364	81.08 342

test20.03		73.75 277	74.59 263	71.22 316	81.11 306	51.12 341	70.15 336	72.10 336	70.42 212	80.28 274	91.50 163	64.21 252	74.72 343	46.96 353	94.58 180	87.82 265

patch_mono-2		78.89 221	79.39 216	77.41 277	84.78 262	68.11 193	75.60 306	83.11 265	60.96 293	79.36 280	89.89 208	75.18 176	72.97 344	73.32 185	92.30 226	91.15 205

pmmvs3		62.47 326	60.02 338	69.80 320	71.58 370	64.00 224	70.52 334	58.44 370	39.77 368	66.05 349	75.84 356	27.10 379	72.28 345	46.15 354	84.77 324	73.11 356

Anonymous20231206		71.38 295	71.88 289	69.88 319	86.31 243	54.37 315	70.39 335	74.62 315	52.57 337	76.73 298	88.76 225	59.94 276	72.06 346	44.35 358	93.23 207	83.23 316

new-patchmatchnet		70.10 302	73.37 275	60.29 349	81.23 305	16.95 379	59.54 359	74.62 315	62.93 276	80.97 261	87.93 238	62.83 264	71.90 347	55.24 316	95.01 167	92.00 185

dp		60.70 335	60.29 337	61.92 345	72.04 369	38.67 372	70.83 332	64.08 360	51.28 346	60.75 363	77.28 351	36.59 366	71.58 348	47.41 350	62.34 371	75.52 353

UnsupCasMVSNet_bld		69.21 309	69.68 304	67.82 330	79.42 325	51.15 340	67.82 345	75.79 308	54.15 328	77.47 297	85.36 282	59.26 282	70.64 349	48.46 346	79.35 347	81.66 333

test-LLR		67.21 314	66.74 318	68.63 327	76.45 346	55.21 311	67.89 342	67.14 354	62.43 282	65.08 355	72.39 360	43.41 351	69.37 350	61.00 283	84.89 320	81.31 337

test-mter		65.00 324	63.79 327	68.63 327	76.45 346	55.21 311	67.89 342	67.14 354	50.98 348	65.08 355	72.39 360	28.27 376	69.37 350	61.00 283	84.89 320	81.31 337

XXY-MVS		74.44 273	76.19 249	69.21 323	84.61 264	52.43 330	71.70 330	77.18 300	60.73 296	80.60 266	90.96 180	75.44 172	69.35 352	56.13 308	88.33 285	85.86 284

UnsupCasMVSNet_eth		71.63 294	72.30 287	69.62 321	76.47 345	52.70 328	70.03 337	80.97 281	59.18 302	79.36 280	88.21 233	60.50 270	69.12 353	58.33 298	77.62 354	87.04 272

WTY-MVS		67.91 313	68.35 311	66.58 334	80.82 311	48.12 352	65.96 349	72.60 332	53.67 331	71.20 333	81.68 324	58.97 284	69.06 354	48.57 345	81.67 338	82.55 322

test0.0.03 1		64.66 325	64.36 326	65.57 337	75.03 357	46.89 358	64.69 351	61.58 366	62.43 282	71.18 334	77.54 348	43.41 351	68.47 355	40.75 364	82.65 335	81.35 336

CHOSEN 280x420		59.08 336	56.52 341	66.76 333	76.51 344	64.39 220	49.62 367	59.00 368	43.86 364	55.66 372	68.41 365	35.55 367	68.21 356	43.25 359	76.78 357	67.69 363

YYNet1		70.06 303	70.44 298	68.90 324	73.76 361	53.42 323	58.99 362	67.20 353	58.42 306	87.10 155	85.39 280	59.82 278	67.32 357	59.79 291	83.50 329	85.96 281

MDA-MVSNet_test_wron		70.05 304	70.44 298	68.88 325	73.84 360	53.47 321	58.93 363	67.28 352	58.43 305	87.09 156	85.40 279	59.80 279	67.25 358	59.66 292	83.54 328	85.92 283

EMVS		61.10 333	60.81 334	61.99 344	65.96 375	55.86 306	53.10 366	58.97 369	67.06 247	56.89 371	63.33 368	40.98 356	67.03 359	54.79 319	86.18 308	63.08 364

testgi		72.36 287	74.61 261	65.59 336	80.56 315	42.82 369	68.29 341	73.35 327	66.87 249	81.84 249	89.93 206	72.08 215	66.92 360	46.05 355	92.54 223	87.01 273

EPMVS		62.47 326	62.63 330	62.01 343	70.63 371	38.74 371	74.76 314	52.86 374	53.91 330	67.71 346	80.01 337	39.40 359	66.60 361	55.54 313	68.81 369	80.68 346

PMMVS		61.65 329	60.38 335	65.47 338	65.40 376	69.26 183	63.97 353	61.73 365	36.80 371	60.11 365	68.43 364	59.42 280	66.35 362	48.97 344	78.57 351	60.81 366

E-PMN		61.59 330	61.62 332	61.49 346	66.81 374	55.40 309	53.77 365	60.34 367	66.80 250	58.90 368	65.50 367	40.48 358	66.12 363	55.72 310	86.25 307	62.95 365

PVSNet_0	51.08 22	56.10 337	54.97 342	59.48 350	75.12 356	53.28 324	55.16 364	61.89 363	44.30 363	59.16 366	62.48 369	54.22 309	65.91 364	35.40 369	47.01 372	59.25 368

sss		66.92 315	67.26 315	65.90 335	77.23 338	51.10 342	64.79 350	71.72 340	52.12 342	70.13 337	80.18 336	57.96 291	65.36 365	50.21 338	81.01 343	81.25 339

TESTMET0.1,1		61.29 331	60.32 336	64.19 340	72.06 368	51.30 338	67.89 342	62.09 362	45.27 361	60.65 364	69.01 363	27.93 377	64.74 366	56.31 307	81.65 340	76.53 351

ADS-MVSNet		61.90 328	62.19 331	61.03 348	73.16 364	36.42 373	67.10 346	61.75 364	49.74 354	66.04 350	82.97 309	46.71 327	63.21 367	42.29 360	69.96 366	83.46 310

DSMNet-mixed		60.98 334	61.61 333	59.09 351	72.88 366	45.05 363	74.70 315	46.61 378	26.20 372	65.34 353	90.32 197	55.46 305	63.12 368	41.72 362	81.30 342	69.09 362

Patchmatch-test		65.91 322	67.38 314	61.48 347	75.51 352	43.21 368	68.84 339	63.79 361	62.48 280	72.80 326	83.42 306	44.89 348	59.52 369	48.27 348	86.45 304	81.70 332

N_pmnet		70.20 300	68.80 309	74.38 301	80.91 308	84.81 40	59.12 361	76.45 306	55.06 324	75.31 315	82.36 318	55.74 303	54.82 370	47.02 351	87.24 299	83.52 309

wuyk23d		75.13 262	79.30 217	62.63 342	75.56 351	75.18 128	80.89 238	73.10 330	75.06 152	94.76 12	95.32 36	87.73 42	52.85 371	34.16 370	97.11 86	59.85 367

PMMVS2		55.64 339	59.27 339	44.74 355	64.30 377	12.32 380	40.60 368	49.79 376	53.19 333	65.06 357	84.81 291	53.60 311	49.76 372	32.68 372	89.41 273	72.15 357

new_pmnet		55.69 338	57.66 340	49.76 354	75.47 353	30.59 376	59.56 358	51.45 375	43.62 365	62.49 361	75.48 357	40.96 357	49.15 373	37.39 368	72.52 361	69.55 361

MVE	40.22 23	51.82 340	50.47 343	55.87 352	62.66 378	51.91 333	31.61 370	39.28 379	40.65 367	50.76 373	74.98 359	56.24 302	44.67 374	33.94 371	64.11 370	71.04 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		30.46 341	29.60 344	33.06 356	17.99 380	3.84 382	13.62 371	73.92 321	2.79 374	18.29 376	53.41 371	28.53 375	43.25 375	22.56 373	35.27 374	52.11 371

DeepMVS_CX				24.13 357	32.95 379	29.49 377		21.63 382	12.07 373	37.95 374	45.07 372	30.84 372	19.21 376	17.94 375	33.06 375	23.69 372

tmp_tt		20.25 343	24.50 346	7.49 358	4.47 381	8.70 381	34.17 369	25.16 381	1.00 376	32.43 375	18.49 373	39.37 360	9.21 377	21.64 374	43.75 373	4.57 373

test123		6.27 346	8.08 349	0.84 359	1.11 383	0.57 383	62.90 354	0.82 383	0.54 377	1.07 379	2.75 378	1.26 382	0.30 378	1.04 376	1.26 377	1.66 374

testmvs		5.91 347	7.65 350	0.72 360	1.20 382	0.37 384	59.14 360	0.67 384	0.49 378	1.11 378	2.76 377	0.94 383	0.24 379	1.02 377	1.47 376	1.55 375

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

cdsmvs_eth3d_5k		20.81 342	27.75 345	0.00 361	0.00 384	0.00 385	0.00 372	85.44 242	0.00 379	0.00 380	82.82 313	81.46 116	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		6.41 345	8.55 348	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	76.94 162	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

ab-mvs-re		6.65 344	8.87 347	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	79.80 339	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

FOURS1					96.08 12	87.41 13	96.19 2	95.83 4	92.95 2	96.57 2

test_one_0601					93.85 63	73.27 138		94.11 37	86.57 27	93.47 40	94.64 58	88.42 27

eth-test2					0.00 384

eth-test					0.00 384

RE-MVS-def			92.61 5		94.13 55	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 65	90.64 11		87.16 28	97.60 67	92.73 151

IU-MVS					94.18 50	72.64 144		90.82 147	56.98 317	89.67 110				85.78 48	97.92 48	93.28 130

save fliter					93.75 64	77.44 104	86.31 126	89.72 177	70.80 208

test0726					94.16 53	72.56 148	90.63 46	93.90 46	83.61 47	93.75 31	94.49 62	89.76 19

GSMVS																83.88 302

test_part2					93.86 62	77.77 98				92.84 49

sam_mvs1												46.11 331				83.88 302

sam_mvs												45.92 336

MTGPA								91.81 121

MTMP							90.66 44	33.14 380

test9_res														80.83 100	96.45 110	90.57 221

agg_prior2														79.68 113	96.16 122	90.22 228

test_prior4						78.97 85	84.59 148

test_prior2							83.37 183		75.43 146	84.58 204	91.57 160	81.92 110		79.54 115	96.97 89

新几何2							81.72 225

旧先验1					91.97 113	71.77 161		81.78 276			91.84 153	73.92 191			93.65 199	83.61 308

原ACMM2							82.26 218

test222					93.31 75	76.54 117	79.38 257	77.79 297	52.59 336	82.36 240	90.84 184	66.83 242			91.69 241	81.25 339

segment_acmp												81.94 107

testdata1							79.62 252		73.95 164

plane_prior7					93.45 70	77.31 108

plane_prior6					92.61 91	76.54 117						74.84 180

plane_prior4											92.95 120

plane_prior3						76.85 115			77.79 118	86.55 169

plane_prior2							89.45 75		79.44 97

plane_prior1					92.83 89

plane_prior						76.42 121	87.15 110		75.94 140						95.03 166

n2								0.00 385

nn								0.00 385

door-mid								74.45 318

test11								91.46 128

door								72.57 333

HQP5-MVS						70.66 171

HQP-NCC					91.19 144		84.77 143		73.30 174	80.55 269

ACMP_Plane					91.19 144		84.77 143		73.30 174	80.55 269

BP-MVS														77.30 144

HQP3-MVS								92.68 99							94.47 182

HQP2-MVS												72.10 213

NP-MVS					91.95 114	74.55 130					90.17 203

MDTV_nov1_ep13_2view						27.60 378	70.76 333		46.47 359	61.27 362		45.20 344		49.18 343		83.75 307

ACMMP++_ref															95.74 144

ACMMP++															97.35 79

Test By Simon												79.09 138

High-resolution multi-view benchmark